Regression Modeling.
Develop, evaluate, and apply bivariate and multivariate linear regression models.
The Excel file for this assignment contains a database with information about the tax assessment value assigned to medical office buildings in a city. The following is a list of the variables in the database:
- FloorArea: square feet of floor space
- Offices: number of offices in the building
- Entrances: number of customer entrances
- Age: age of the building (years)
- AssessedValue: tax assessment value (thousands of dollars)
Use Excel’s Analysis ToolPak to conduct a regression analysis with AssessmentValue as the dependent variable and FloorArea, Offices, Entrances, and Age as independent variables. What is the overall fit r^2? What is the adjusted r^2?
Which predictors are considered significant if we work with α=0.05? Which predictors can be eliminated?
What is the final model if we only use FloorArea and Offices as predictors?
Suppose our final model is:
AssessedValue = 115.9 + 0.26 x FloorArea + 78.34 x Offices
What wouldbe the assessed value of a medical office building with a floor area of 3500 sq. ft., 2 offices, that was built 15 years ago? Is this assessed value consistent with what appears in the database?